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首页> 外文期刊>Journal of Medical Systems >Classification of the Colonic Polyps in CT-Colonography Using Region Covariance as Descriptor Features of Suspicious Regions
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Classification of the Colonic Polyps in CT-Colonography Using Region Covariance as Descriptor Features of Suspicious Regions

机译：CT结肠造影中结肠息肉的分类，使用区域协方差作为可疑区域的特征

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摘要

We present an algorithm to classify polyps in CT colonography images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, they cannot simply be fed to traditional machine learning tools such as support vector machines (SVMs) or artificial neural networks (ANNs). To benefit from the simple yet one of the most powerful nonparametric machine learning approach k-nearest neighbor classifier, it suffices to compute the pairwise distances among the covariance descriptors using a distance metric involving their generalized eigenvalues, which also follows from the Lie group structure of positive definite matrices. This approach is fast and discriminates polyps from non-polyps with high accuracy using only a small size descriptor, which consists of 36 unique features per image region extracted from the suspicious regions that we have obtained by combined cellular neural network (CNN) and template matching detection method. These suspicious regions are, in average, 15 × 17 = 255 pixels in our experiments.
机译：我们提出一种算法，利用协方差矩阵作为对象描述符对CT结肠造影图像中的息肉进行分类。由于这些描述符不位于向量空间上，因此不能简单地将它们提供给传统的机器学习工具，例如支持向量机（SVM）或人工神经网络（ANN）。为了从最简单但功能最强大的非参数机器学习方法k近邻分类器中受益，使用涉及其广义特征值的距离量度来计算协方差描述符之间的成对距离就足够了，这也遵循Lie的Lie组结构正定矩阵。这种方法快速且仅使用小尺寸描述符就可以高精度地将息肉与非息肉区分开，该描述符包含每个图像区域的36个独特特征，这些特征是通过结合细胞神经网络（CNN）和模板匹配从可疑区域提取的检测方法。在我们的实验中，这些可疑区域平均为15×17 = 255像素。

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